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Dynamic Factor Models

Author

Listed:
  • Catherine Doz

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Peter Fuleky

    (University of Hawaii)

Abstract

Dynamic factor models are parsimonious representations of relationships among time series variables. With the surge in data availability, they have proven to be indispensable in macroeconomic forecasting. This chapter surveys the evolution of these models from their pre-big-data origins to the large-scale models of recent years. We review the associated estimation theory, forecasting approaches, and several extensions of the basic framework.

Suggested Citation

  • Catherine Doz & Peter Fuleky, 2020. "Dynamic Factor Models," PSE-Ecole d'économie de Paris (Postprint) halshs-02491811, HAL.
    • Handle: RePEc:hal:pseptp:halshs-02491811
    DOI: 10.1007/978-3-030-31150-6_2
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    Citations

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    Cited by:

    1. Peter Fuleky, 2022. "Nowcasting the trajectory of the COVID-19 recovery," Applied Economics Letters, Taylor & Francis Journals, vol. 29(11), pages 1037-1041, June.
    2. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    3. Chiara Casoli & Riccardo (Jack) Lucchetti, 2022. "Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices [Commodity-price comovement and global economic activity]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 494-514.
    4. Priscila Espinosa & Jose M. Pavía, 2023. "Automation in Regional Economic Synthetic Index Construction with Uncertainty Measurement," Forecasting, MDPI, vol. 5(2), pages 1-19, April.
    5. Boriss Siliverstovs & Daniel S. Wochner, 2021. "State‐dependent evaluation of predictive ability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 547-574, April.
    6. Casoli, Chiara & Lucchetti, Riccardo (Jack), 2021. "Permanent-Transitory decomposition of cointegrated time series via Dynamic Factor Models, with an application to commodity prices," FEEM Working Papers 312367, Fondazione Eni Enrico Mattei (FEEM).
    7. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022. "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 500-519.
    8. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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